pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse, ggthemes, DT)Hands-on Exercise 03.b
4.1 Overview
When telling a visually-driven data story, animated graphics tends to attract the interest of the audience and make deeper impression than static graphics. In this hands-on exercise, we will learn how to:
- Create animated data visualisation by using
gganimateandplotlyr packages - Reshape data by using
tidyversepackage - Process, wrangle and transform data by using
dplyrpackage
4.1.1 Basic concepts of animation
When creating animations, the plot does not actually move. Instead, many individual plots are built and then stitched together as movie frames, just like an old-school flip book or cartoon. Each frame is a different plot when conveying motion, which is built using some relevant subset of the aggregate data. The subset drives the flow of the animation when stitched back together.

4.1.2 Terminology
Before we dive into the steps for creating an animated statistical graph, it’s important to understand some of the key concepts and terminology related to this type of visualization.
Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.
Animation Attributes: The animation attributes are the settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.
Before you start making animated graphs, you should first ask yourself: Does it makes sense to go through the effort? If you are conducting an exploratory data analysis, a animated graphic may not be worth the time investment. However, if you are giving a presentation, a few well-placed animated graphics can help an audience connect with your topic remarkably better than static counterparts.
4.2 Getting Started
4.2.1 Loading the R packages
First, write a code chunk to check, install and load the following R packages:
plotly, R library for plotting interactive statistical graphs.
gganimate, an ggplot extension for creating animated statistical graphs.
gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
gapminder: An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.
tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
4.2.2 Importing and Examing the data
In this hands-on exercise, the Data worksheet from GlobalPopulation Excel workbook will be used.
We first use read_xls of tidyverse package to import the document:
globalpop_raw <- read_xls("data/GlobalPopulation.xls",sheet="Data") Next, we use function str() , head() from R, function summarise_all() and n_distinct() from dplyr to examine the data structure and variable types:
- The dataset contains 6,204 observations with no missing values
Country: The dataset contains 222 countries stored as character data type
Year: The data spans from 1996 to 1950 and stored as double data type
Young: Based on the data context, the “Young” variable represents the percentage of young people in the population with values ranging from 15.5% to 109.2%. It stored as double data type
Old: Based on the data context, the “Old” variable represents the percentage of elderly people in the population with values ranging from 1% to 77.1% It stored as double data type
Population: The values ranging from 3 K to 1,807,878.6 K within the data period
Continent: The dataset contains 6 continenet stored as character data type
str(globalpop_raw)tibble [6,204 × 6] (S3: tbl_df/tbl/data.frame)
$ Country : chr [1:6204] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
$ Year : num [1:6204] 1996 1998 2000 2002 2004 ...
$ Young : num [1:6204] 83.6 84.1 84.6 85.1 84.5 84.3 84.1 83.7 82.9 82.1 ...
$ Old : num [1:6204] 4.5 4.5 4.5 4.5 4.5 4.6 4.6 4.6 4.6 4.7 ...
$ Population: num [1:6204] 21560 22913 23898 25268 28514 ...
$ Continent : chr [1:6204] "Asia" "Asia" "Asia" "Asia" ...
head(globalpop_raw)# A tibble: 6 × 6
Country Year Young Old Population Continent
<chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 Afghanistan 1996 83.6 4.5 21560. Asia
2 Afghanistan 1998 84.1 4.5 22913. Asia
3 Afghanistan 2000 84.6 4.5 23898. Asia
4 Afghanistan 2002 85.1 4.5 25268. Asia
5 Afghanistan 2004 84.5 4.5 28514. Asia
6 Afghanistan 2006 84.3 4.6 31057 Asia
globalpop_raw %>%
summarise_all(~n_distinct(.))# A tibble: 1 × 6
Country Year Young Old Population Continent
<int> <int> <int> <int> <int> <int>
1 222 28 819 589 5791 6
# check if there're any missing values
any(is.na(globalpop_raw))[1] FALSE
summary(globalpop_raw) Country Year Young Old
Length:6204 Min. :1996 Min. : 15.50 Min. : 1.00
Class :character 1st Qu.:2010 1st Qu.: 25.70 1st Qu.: 6.90
Mode :character Median :2024 Median : 34.30 Median :12.80
Mean :2023 Mean : 41.66 Mean :17.93
3rd Qu.:2038 3rd Qu.: 53.60 3rd Qu.:25.90
Max. :2050 Max. :109.20 Max. :77.10
Population Continent
Min. : 3.3 Length:6204
1st Qu.: 605.9 Class :character
Median : 5771.6 Mode :character
Mean : 34860.9
3rd Qu.: 22711.0
Max. :1807878.6
4.2.3 Handling Data Issues
4.2.3.1 Data Type Issues
- Year: Since year is a whole number rather than a decimal, we should transform its data type from double <dbl> to integer<int>.
- Country and Continent: Since these two categorical variables will be analyzed further, we need to transform their data type from character <chr> to factor<fctr>. In R, factors are used to handle categorical data and ordered variable.
Here, we use mutate_each_() of dplyr package to convert all character data type into factor, and use mutate of dplyr package to convert data values of Year field into integer.
col <- c("Country","Continent")
globalpop_raw <- read_xls("data/GlobalPopulation.xls",sheet="Data") %>%
mutate_each_(funs(factor(.)),col) %>%
mutate(Year = as.integer(Year))
head(globalpop_raw)# A tibble: 6 × 6
Country Year Young Old Population Continent
<fct> <int> <dbl> <dbl> <dbl> <fct>
1 Afghanistan 1996 83.6 4.5 21560. Asia
2 Afghanistan 1998 84.1 4.5 22913. Asia
3 Afghanistan 2000 84.6 4.5 23898. Asia
4 Afghanistan 2002 85.1 4.5 25268. Asia
5 Afghanistan 2004 84.5 4.5 28514. Asia
6 Afghanistan 2006 84.3 4.6 31057 Asia
Unfortunately, mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0. In view of this, we will re-write the code by using mutate_at() as shown in the code chunk below.
col <- c("Country","Continent")
globalpop_raw <- read_xls("data/GlobalPopulation.xls",sheet="Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))
head(globalpop_raw)# A tibble: 6 × 6
Country Year Young Old Population Continent
<fct> <int> <dbl> <dbl> <dbl> <fct>
1 Afghanistan 1996 83.6 4.5 21560. Asia
2 Afghanistan 1998 84.1 4.5 22913. Asia
3 Afghanistan 2000 84.6 4.5 23898. Asia
4 Afghanistan 2002 85.1 4.5 25268. Asia
5 Afghanistan 2004 84.5 4.5 28514. Asia
6 Afghanistan 2006 84.3 4.6 31057 Asia
Instead of using mutate_at(), across() can be used to derive the same outputs.
col <- c("Country","Continent")
globalpop_raw <- read_xls("data/GlobalPopulation.xls",sheet="Data") %>%
mutate(across(all_of(col), as.factor)) %>%
mutate(Year = as.integer(Year))
head(globalpop_raw)# A tibble: 6 × 6
Country Year Young Old Population Continent
<fct> <int> <dbl> <dbl> <dbl> <fct>
1 Afghanistan 1996 83.6 4.5 21560. Asia
2 Afghanistan 1998 84.1 4.5 22913. Asia
3 Afghanistan 2000 84.6 4.5 23898. Asia
4 Afghanistan 2002 85.1 4.5 25268. Asia
5 Afghanistan 2004 84.5 4.5 28514. Asia
6 Afghanistan 2006 84.3 4.6 31057 Asia
4.2.3.2 Data Quality Issues
The data summary statistics show that the maximum value of Young% is 109.2%, indicating inaccurate or incomplete data. This is problematic because Young% + Old% should be less than or equal to 100%. A value exceeding this would imply a negative Mid-aged%, which is neraly impossible and suggests inaccurate or missing data in this dataset.
summary(globalpop_raw) Country Year Young Old
Afghanistan: 28 Min. :1996 Min. : 15.50 Min. : 1.00
Albania : 28 1st Qu.:2010 1st Qu.: 25.70 1st Qu.: 6.90
Algeria : 28 Median :2024 Median : 34.30 Median :12.80
Andorra : 28 Mean :2023 Mean : 41.66 Mean :17.93
Angola : 28 3rd Qu.:2038 3rd Qu.: 53.60 3rd Qu.:25.90
Anguilla : 28 Max. :2050 Max. :109.20 Max. :77.10
(Other) :6036
Population Continent
Min. : 3.3 Africa :1568
1st Qu.: 605.9 Asia :1454
Median : 5771.6 Europe :1344
Mean : 34860.9 North America: 976
3rd Qu.: 22711.0 Oceania : 526
Max. :1807878.6 South America: 336
Below are 63 observations with data accuracy issue. To maintain dataset’s integrity, we should remove these problematic countries records.
dq_issues <- subset(globalpop_raw, Young > 100 | (Young + Old) > 100)
table <- DT::datatable(dq_issues, class= "display",
caption = "Table 1: Observations with data quality issues") %>%
formatStyle(
columns = colnames(dq_issues),
fontSize = '12px',
fontFamily = 'Helvetica',
lineHeight = '1.2'
)
tableAfter remove problematic records, there are 5,953 observations remained with 213 distinct countries.
c_removed = unique(dq_issues$Country)
globalPop <- subset(globalpop_raw, !(Country %in% c_removed))
str(globalPop)tibble [5,953 × 6] (S3: tbl_df/tbl/data.frame)
$ Country : Factor w/ 222 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Year : int [1:5953] 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 ...
$ Young : num [1:5953] 83.6 84.1 84.6 85.1 84.5 84.3 84.1 83.7 82.9 82.1 ...
$ Old : num [1:5953] 4.5 4.5 4.5 4.5 4.5 4.6 4.6 4.6 4.6 4.7 ...
$ Population: num [1:5953] 21560 22913 23898 25268 28514 ...
$ Continent : Factor w/ 6 levels "Africa","Asia",..: 2 2 2 2 2 2 2 2 2 2 ...
summary(globalPop) Country Year Young Old
Afghanistan: 28 Min. :1996 Min. :15.50 Min. : 1.00
Albania : 28 1st Qu.:2010 1st Qu.:25.50 1st Qu.: 7.10
Algeria : 28 Median :2024 Median :33.40 Median :13.70
Andorra : 28 Mean :2023 Mean :40.19 Mean :18.39
Angola : 28 3rd Qu.:2038 3rd Qu.:50.90 3rd Qu.:26.50
Anguilla : 28 Max. :2050 Max. :94.80 Max. :77.10
(Other) :5785
Population Continent
Min. : 3.3 Africa :1372
1st Qu.: 597.8 Asia :1399
Median : 5580.3 Europe :1344
Mean : 35028.8 North America: 976
3rd Qu.: 22093.1 Oceania : 526
Max. :1807878.6 South America: 336
globalPop %>% summarise_all(~n_distinct(.))# A tibble: 1 × 6
Country Year Young Old Population Continent
<int> <int> <int> <int> <int> <int>
1 213 28 756 589 5549 6
4.3 Animated Data Visualisation: gganimate methods
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.
transition_*()defines how the data should be spread out and how it relates to itself across time.view_*()defines how the positional scales should change along the animation.shadow_*()defines how data from other points in time should be presented in the given point in time.enter_*()/exit_*()defines how new data should appear and how old data should disappear during the course of the animation.ease_aes()defines how different aesthetics should be eased during transitions.
4.3.1 Building a static population bubble plot
In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country))+
geom_point(alpha=0.7, show.legend = FALSE)+
scale_colour_manual(values = country_colors)+
scale_size(range= c(2,12))+
labs(title = 'Global Population Change from 1996 to 2050',
subtitle = 'Year:{frame_time}',
x = '% Aged',
y = '% Young')+
theme_economist(base_size = 8)
4.3.2 Building the animated bubble plot
In the code chunk below,
transition_time()of gganimate is used to create transition through distinct states in time (i.e. Year).frame_timeis a special placeholder (dynamic title) in gganimate.ease_aes()is used to control easing of aesthetics. The default islinear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.ggplot(globalPop, aes(x = Old, y= Young, size = Population, colour = Country))+ geom_point(alpha = 0.7, show.legend = FALSE)+ scale_colour_manual(values = country_colors)+ scale_size(range = c(2,12))+ # control point size to be 2~12 labs(title = 'Global Population Change from 1996 to 2050', subtitle = 'Year:{frame_time}', # {frame_time} is a special placeholder (dynamic title) in gganimate x = '% Aged', y = '% Young')+ transition_time(Year)+ ease_aes('cubic-in-out')+ theme_economist(base_size = 8)
4.4 Animated Data Visualisation: plotly
In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).
4.4.1 Building an animated bubble plot: ggplotly() method
In this sub-section, we will create an animated bubble plot by using ggplotly() method.
Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.
ggplotly()is then used to convert the R graphic object into an animated svg object.
gg <- ggplot(globalPop, aes(x = Old, y = Young,
size = Population, colour = Country))+
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors)+
# control the size of points from 2 to 12
scale_size(range = c(2,12))+
labs(title = 'Global Population Change from 1996 to 2050',
x = '% Aged',
y = '% Young')+
theme_wsj(base_size = 8) +
theme(axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"))
ggplotly(gg)4.4.1.1 Improvements Needed
- Legend: Notice that although
show.legend = FALSEargument was used, the legend still appears on the plot. To overcome this problem,theme(legend.position='none')should be used as shown in the plot and code chunk below. - Color: Although the “
country_colors” palette from gapminder provides colors for 142 countries, our dataset contains 213 countries, causing many data points to appear in grey. To improve visual distinction, we should color code the data by “Continent” rather than “Country”. - Tooltips: To improve the readability of the plot, tooltips are customized with detailed infomations by using
text().
gg <- ggplot(globalPop, aes(x = Old, y = Young,
size = Population, colour = Continent,
text = paste("Year:",Year, # customize the content in tooltips
"<br>Continent:",Continent,
"<br>Country:", Country,
"<br>Population:", scales::comma(Population), "K",
"<br>Old:",round(Old,2),"%",
"<br>Young:",round(Young,2),"%")))+
geom_point(aes(frame = Year),alpha = 0.7) +
scale_size(range = c(2,12))+
labs(title = 'Global Population Change from 1996 to 2050',
x = '% Aged',
y = '% Young')+
theme_wsj(base_size = 8)+ scale_color_wsj()+
theme(axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
legend.position = 'none') # remove legend
ggplotly(gg, tooltip = "text")4.4.2 Building an animated bubble plot: plot_ly() method
In this sub-section, you will learn how to create an animated bubble plot by using plot_ly() method.
To customized the layout of plot_ly(), we need to use layout().
bp <- globalPop %>%
plot_ly(x = ~Old, y= ~Young,
size = ~Population, color = ~Continent,
sizes = c(2,100),
frame = ~Year, text = ~Country,
hoverinfo = "text",
type = "scatter", mode = "markers") %>%
layout(showlegend = FALSE,
title = list (text = "Global Population Change from 1996 to 2050",
font = list(size = 15,family="Georgia", face = "bold")),
xaxis = list(title = "% Aged",
titlefont = list(size = 12, family = "Georgia")),
yaxis = list(title = "% Young",
titlefont = list(size = 12, family = "Georgia")),
plot_bgcolor = "#f3f1e9",
paper_bgcolor = "#f3f1e9")
bp